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Month: April 2016

We presented our progress since last week. We made some adjustments based on the remarks that were given after last week’s presentation, some of these adjustments were already scheduled to happen.

First of all we implemented the fixed position for the search box, this was a remark that was given during the last presentation. Secondly, we added the axes back to the multiples view.

Finally, we incorporated region and world data into our design. The World Bank dataset does not provide data for geographical continents, but features certain regions constructed on their similarity in geographical position, culture and economical conditions. We hence use these regions in our design.

Some interesting remarks were providing after our presentation:

The inclusion of the axes is certainly a positive adjustment. It might also be good to put the x-axis on the same level for each graph and scale each graph the same to be able to compare them to each other more easily. We looked into this possiblity: for series expressed in percentages, this is a possibility we will look into. Nevertheless, for series expressed in absolute numbers, scaling them the same is a problem as visible in Figure 1. We will look into the possibility of expressing these in a logaritmic scale.

problems with global scale for series in absolute numbers

Some remarks were about the graphs themselves: the dots are too small to easily pull up the tooltip, maybe another type of graph could work better, the usage of green and red for positive and negative numbers on the graph, to reduce the numbers shown after the decimal, etc. Increasing the size of the dots is not an option, the line graph will become too distorted and the goal of the visualization is not to see the exact number but the trend. Incorporating green/red for positive/negative values is not a good add-on in our eyes. First of, we use colors to diversify in country/region/world data, this would then no longer be possible. Secondly, it is already very easy to see which values are positive/negative at first glance by their position according to the x-axis which represents the zero value.

a valuable remark was made that some graphs or series had almost no data and that we should explore the possibility of dropping these. We will indeed investigate this.

One final remark was that for some series it is not very clear from their title what they actually entail. The suggestion was made to include a pop-up with further information about them. This information is available in the World Bank database and we will indeed look into adding this feature.

Last class, we presented our current state of the project and the updates we made since the last presentation.

For us, the update is quite big: we changed our data set. As LastFM’s API was still broken after four weeks, we decided to search for a new dataset to save our project. We looked for a dataset that would enable us to keep most of the visualisation we developed already. That way, we did not have to throw all of our work out the window.

We decided upon the World Bank Databank, and more specifically the dataset on World Development Indicators. We picked out a few series that we thought would be interesting to show. The small multiples visualisation was elaborated enough to show the class what we did so far.

There were a lot of interesting and useful design critiques:

A few critiques were about sorting: it would be nice to sort graphs from lowest to highest population growth percentage, sorting according to continent, enable the user to select a few aspects himself to be able to compare. The joint underlying thought was that they felt there were too many small multiples and it was hard to compare different aspects that way. This is why we are currently looking into filtering options, so the user can indeed select what he wants to see.

Some other remarks had to do with the graphs themselves: we should add the begin and end date to the graphs, add axes and show a mean value in the graph (in a different colour). There was a suggestion to use the same scale for every graph, but this is something that needs some further investigation. With our previous dataset, we also tried to do this, but then for some countries there were no trends to be seen because the numbers were too small compared to the maximum used for the scaling. Hence we need to check what the result will be on our current dataset before we take a design decision. Another thing is that for the year 2015, there is not always data present yet in the set, and so the graph often goes to zero, which presents a wrong image. So we need to look into how we will solve this.

Two general design critiques: it would be convenient if the search box stays fixed at the top of the window while scrolling and a ‘back’ button would also be in the interest of the user, as it is apparently not intuitive that you can click on the graphs.

In the mean time, we already implemented the fixed position of the search box. We also gathered aggregate data for different regions. The set does not provide data for the exact continents, but proposes some regions that are economically and culturally similar. We will hence use these regions for the aggregates. The other remarks are still being considered at this point.

Due to time constraints, on Joris’ advice, and because nobody even asked where they went, we decided to leave out the map visualisations and focus on perfecting the small multiples.

We think the way the presentations were organised, was more efficient compared to how it usually goes, although it would have been nice to be able to respond to certain critiques sometimes – it was sometimes very hard not to do so, as observed.

Birgit

As we changed our dataset – more on that in a next post – I started looking at visualisations concerning population growth. I came across this Urban World Visualisation made by Periscopic for Unicef in 2012.

It is based on their report ‘The State of the World’s Children’ from the same year. Periscopic used the global population data from the report to map the growth of over 100 countries since 1950. They also projected the population growth to 2050 and added the percentage of urban population for each country’s total population. The circles are scaled to the urban population size – so the bigger the circle, the higher the real number of urban population. Percentages are shown by means of the colour of the circles.

In this visualisation, I like the fact that it is not a map, although the circles are organised according to the relative geographic position of each country. In that way, you can quickly look up a country if you roughly know where it is situated, as you see a slight form of a map in it.

The nice thing with the scaling is that you can find some interesting insights: you can see e.g. that Mexico has a very high urban population compared to the total population, and compared to neighbour USA. Or that Belgium is 97% urban.